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Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains.
The iCub humanoid robot: an open platform for research in embodied cognition
G. Metta, G. Sandini, D. Vernon, L. Natale, and F. Nori · 2008
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Robustness and regularization of support vector machines
H. Xu, C. Caramanis, and S. Mannor · 2009
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The security of machine learning
M. Barreno, B. Nelson, A. Joseph, and J. Tygar · 2010
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Adversarial machine learning
L. Huang, A. D. Joseph, B. Nelson, B. Rubinstein, and J. D. Tygar · 2011
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Poisoning attacks against support vector machines
B. Biggio, B. Nelson, and P. Laskov · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli · 2013
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Deep learning using support vector machines
Y. Tang · 2013
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Security evaluation of pattern classifiers under attack
B. Biggio, G. Fumera, and F. Roli · 2014
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Probability models for open set recognition
W. Scheirer, L. Jain, and T. Boult · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Practical evasion of a learning-based classifier: A case study
N. Šrndic and P. Laskov · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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One-and-a-half-class multiple classifier systems for secure learning against evasion attacks at test time
B. Biggio, I. Corona, Z.-M. He, P. P. K. Chan, G. Giacinto, D. S. Yeung, and F. Roli · 2015
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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On security and sparsity of linear classifiers for adversarial settings
A. Demontis, P. Russu, B. Biggio, G. Fumera, and F. Roli · 2016
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Adversarial examples detection in deep networks with convolutional filter statistics
X. Li and F. Li · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
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The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
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Y. Luo, X. Boix, G. Roig, T. Poggio, and Q. Zhao · 2015
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Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
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Teaching iCub to recognize objects using deep convolutional neural networks
G. Pasquale, C. Ciliberto, F. Odone, L. Rosasco, L. Natale, and I. dei Sistemi · 2015
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Towards open set deep networks
A. Bendale and T. E. Boult · 2016
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Intelligence reinvented
N. Cristianini · 2016
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Secure kernel machines against evasion attacks
P. Russu, A. Demontis, B. Biggio, G. Fumera, and F. Roli · 2016
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
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A boundary tilting persepective on the phenomenon of adversarial examples
T. Tanay and L. Griffin · 2016
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Improving the robustness of deep neural networks via stability training
S. Zheng, Y. Song, T. Leung, and I. Goodfellow · 2016
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Detecting adversarial samples from artifacts
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner · 2017
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